We need a framework for external safety testing of advanced AI models to understand and mitigate potential dangers before they are released.

Real-world testing is essential for ensuring that AI models are reliable and effective in practical applications.

Testing is essential for ensuring that AI systems behave as expected, not just in terms of performance but also in their underlying behaviors.

In product development, it's vital to communicate the capabilities and limitations of AI models upfront, ensuring users have the right expectations about what the technology can and cannot do.

The AI industry suffers from a lack of rigorous evaluations and tests that demonstrate the frontier of model capabilities.

To effectively test AI control measures, models should be stress-tested by training them to understand the mitigations in place, ensuring they cannot exploit knowledge of monitoring to act harmfully.

AI systems must undergo rigorous internal testing while also engaging with external experts to ensure safety and reliability before public deployment.

Adequate protections are necessary to responsibly deploy advanced AI systems; without them, deployment is not feasible.

As AI systems become more sophisticated, they will carry greater risks, necessitating international dialogue and cooperation to establish standards for their deployment.